Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

2.9K
Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
2.9K
Inertia Tensor01:24

Inertia Tensor

692
The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
692
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.1K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.1K
Singularity Functions for Shear01:26

Singularity Functions for Shear

215
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
215
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

15.8K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
15.8K
Scalar Product (Dot Product)01:11

Scalar Product (Dot Product)

13.2K
The scalar multiplication of two vectors is known as the scalar or dot product. As the name indicates, the scalar product of two vectors results in a number, that is, a scalar quantity. Scalar products are used to define work and energy relations. For example, the work that a force (a vector) performs on an object while causing its displacement (a vector) is defined as a scalar product of the force vector with the displacement vector.
The scalar product of two vectors is obtained by multiplying...
13.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A highly sensitive and specific electrochemical sensing method for robust detection of Escherichia coli lac Z gene sequence.

Biosensors & bioelectronics·2015
Same author

Stereoselective intermolecular nitroaminoxylation of terminal aromatic alkynes: trapping alkenyl radicals by TEMPO.

Organic letters·2014
Same author

Correlation between red blood cell transfusion volume and mortality in patients with massive blood transfusion: A large multicenter retrospective study.

Experimental and therapeutic medicine·2014
Same author

[Sampling survey of schistosomiasis prevention knowledge among middle school students in endemic areas of Hubei Province].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2014
Same author

Synthesis of ultrahighly electron-deficient pyrrolo[3,4-d]pyridazine-5,7-dione by inverse electron demand Diels-Alder reaction and its application as electrochromic materials.

Organic letters·2014
Same author

Quantitative proteomics analysis by iTRAQ in human nuclear cataracts of different ages and normal lens nuclei.

Proteomics. Clinical applications·2014
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

IncTSVD:多维数据流的增量张量单数值分解.

Muhammad A A Abdelgawad, Ray C C Cheung, Hong Yan

    IEEE transactions on neural networks and learning systems
    |June 23, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们介绍了IncTSVD,这是一种在线方法,用于流动张量数据的增量张量奇数值分解 (TSVD). 这种方法有效地处理有限的内存,并减少与现有的张量分解方法相比的计算成本.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    530
    Determining 3D Flow Fields via Multi-camera Light Field Imaging
    14:25

    Determining 3D Flow Fields via Multi-camera Light Field Imaging

    Published on: March 6, 2013

    16.7K

    相关实验视频

    Last Updated: Sep 18, 2025

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.6K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    530
    Determining 3D Flow Fields via Multi-camera Light Field Imaging
    14:25

    Determining 3D Flow Fields via Multi-camera Light Field Imaging

    Published on: March 6, 2013

    16.7K

    科学领域:

    • 数字分析 数字分析
    • 数据科学数据科学数据科学
    • 张量计计算 张量计计算

    背景情况:

    • 张量单数值分解 (TSVD) 对于分析多维数据至关重要.
    • 现有的TSVD方法通常需要大批次计算,限制其使用流数据或有限的内存.
    • 对矩阵的增量奇数值分解 (SVD) 为更新分解提供了一个先例.

    研究的目的:

    • 开发一个在线算法,以在第三阶段张量数序列上逐步计算TSVD.
    • 为了解决基于批次的TSVD方法在传输张量数据和内存约束方面的局限性.
    • 将增量矩阵SVD的概念扩展到张量分析的领域.

    主要方法:

    • 在IncTSVD算法逐步计算TSVD使用张量-张量概念.
    • 它保持了以前数据的基础张量,并将TSVD近似与传入的张量数据更新.
    • 在合成和现实数据集上进行了理论分析和广泛的数值实验.

    主要成果:

    • 与现有的基于t产品的张量分解相比,IncTSVD表现出更高的计算和存储效率.
    • 该方法的准确性与标准TSVD相提并论.
    • 数字实验验证了计算成本和近似误差的理论分析.

    结论:

    • IncTSVD是一种有效的在线增量计算方法,用于流动张量数据的TSVD.
    • 该算法在计算和存储成本方面提供了显著的优势,使其适用于内存有限的环境.
    • IncTSVD为动态数据流提供了批量张量分解技术的可行替代方案.